machine learning algorithms book

machine Learning Algorithms Summary 1. Preface by using the machine learning algorithm to summarize the work, convenient for later search, rapid application. 2. Recommended Algorithms Cross Minimum Variance

Introduction to several common optimization algorithms for machine learning789491451. Gradient Descent method (Gradient descent) 2. Newton's method and Quasi-Newton method (Newton ' s method Quasi-Newton Methods) 3. Conjugate gradient method (conjugate Gradient) 4. Heuristic Optimization Method 5. Solving constrained optimization problems--Lagrange multiplier methodEach of us in our life or work encountere

conjugate gradient method is not only one of the most useful methods to solve the large scale linear equations,is also one of the most effective algorithms for solving large-scale nonlinear optimization. In various optimization algorithms, the conjugate gradient method is very important. Its advantage is that the required storage capacity is small, has step convergence, high stability, and does not require

of the total number of features with non-0 weights)9. Logistic regression : Two-dollar category, extremely efficient Giallo Computer System (many problems need to use probability estimates as output) two ways: "As is" "converted to two-dollar category" Application: Automatic diagnosis of disease (to investigate the risk factors that cause disease, and to predict the probability of disease occurrence according to risk factors), economic forecasts and other fieldsCategory: Evaluation indicators:

)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on

specific job requirements, image algorithm For example, now deep learning hot not I said, so the basic convolution neural network algorithm , image classification , image detection The more famous paper in recent years should read it. If you have a condition, use it like a caffe,tensorflow frame.2. Machine Learning EngineerThis post is basically the same as the

Machine learning algorithms are numerous, and various algorithms involve more parameters, this article will briefly introduce the RF,GBDT and other algorithms of tuning experience and steps. 1. BP Tuning matters1.BP is sensitive to feature scaling, first scale data.2. Experi

KNN algorithm of ten Algorithms for machine learningThe previous period of time has been engaged in tkinter, machine learning wasted a while. Now want to re-write one, found a lot of problems, but eventually solved. We hope to make progress together with you.Gossip less, get to the point.KNN algorithm, also called near

classes more equal. but .....Recall, though,that better data often beats better algorithms, and designing good features goes a long. And if you had a huge dataset, your choice of classification algorithm might not really matter so much in terms of Classi Fication performance (so choose your algorithm based on speed or ease of use instead).And if you really-accuracy, you should definitely try a bunch of different classifiers and select the best one by

SAS graphical user interfaces help you build machine-learning models and implement an iterative machine learning process. You don ' t have a advanced statistician. Our comprehensive selection of the machine learning

Nine algorithms for machine learning---naive Bayesian classifierTo understand the Naive Bayes classificationBayesian classification is a generic term for a class of classification algorithms, which are based on Bayesian theorem, so collectively referred to as Bayesian classification. Naive naive Bayesian classification

hope for in the earthquake prediction is that the recall is very high, that is to say, every earthquake we want to predict. We can sacrifice precision at this time. 1000 alarms are preferred, 10 earthquakes are predicted correctly, and do not predict 100 times 8 leaks two times. Suspects convictedBased on the principle of not blaming a good man, we hope to be very accurate about the conviction of a suspect. In time, some criminals were spared (recall low), but also worthwhile. Regressi

value;If it becomes smaller, the new puzzle will replace the original;If it becomes larger, the probability of replacing the old one with the new one depends on the current temperature value, where the temperature will begin to slow down at a relatively high value, which is why the algorithm is more receptive to relatively poor performance in the early stages of execution, so that we can effectively avoid the possibility of falling into the local minimum, when the temperature reaches 0, The alg

Perception Machine: This is the simplest machine learning algorithm, but there are a few points to note. The first is the selection of the loss function, and in order to minimize the loss function, the gradient descent method used in the iterative process, finally obtains the optimal w,bThe visual interpretation is to adjust the value of the w,b, so that the sepa

A simple introduction to machine learning algorithms.As the team (Big Data Team) technology development needs, through the traffic business data needs to expand, to achieve data mining and data analysis technology mastery, bypassing the machine learning algorithm, it can be said that the core value of big data lies in

. Or after the derivation of the formula can not be interpreted, or the number of unknown parameters is greater than the number of equations. At this point, the iterative algorithm is used to find the optimal solution step-after-step. In particular, if the optimization function is a convex function, then there is a global optimal solution, if the function is non-convex, then there will be many local optimal solutions, so the importance of convex optimization is self-evident. People always wan

]) *double (Dy[i])#Sqx = double (Dx[i]) **2Sumxy= VDOT (Dx,dy)#returns the point multiplication of two vectors multiplySQX = SUM (Power (dx,2))#Square of the vector: (x-meanx) ^2#calculate slope and interceptA = sumxy/SQXB= meany-a*MeanxPrintA, b#Draw a graphicPlotscatter (XMAT,YMAT,A,B,PLT)7.1.4 Normal Equation Group methodCode implementation of 7.1.5 normal equation set#data Matrix, category labelsXarr,yarr = Loaddataset ("Regdataset.txt")#Importing Data Filesm= Len (Xarr)#generate x-coordinat

value of 3.For example: Np.random.randint (3, 6, size=[2,3]) returns data with a dimension of 2x3. The value range is [3,6].(4). Random_integers (low[, high, size]), similar to the above randint, the difference between the range of values is closed interval [low, high].(5). Random_sample ([size]), returns the random floating-point number in the half-open interval [0.0, 1.0]. If it is another interval [a, b), it can be converted (b-a) * Random_sample ([size]) + AFor example: (5-2) *np.random.ran

In the process of machine learning, we often meet the problem of fitting. The high dimension of input data or features is one of the problems that lead to overfitting. The higher the dimension, the more sparse your data will be in each feature dimension, which is basically catastrophic for machine learning

Recently learned about Python implementation of common machine learning algorithms on GitHubDirectory First, linear regression 1. Cost function2. Gradient Descent algorithm3. Normalization of the mean value4. Final running result5, using the linear model in the Scikit-learn library to implement Second, logistic regression 1. Cost funct